方差分析和画图
# 颜色
gf_color <- c("#A1D99B","#41AB5D","#9ECAE1","#4292C6","#FDAE6B","#F16913","#BCBDDC","#807DBA")
# 绘图
ggthemr('fresh',layout = "clean")
# layout(matrix(c(1,1,2,3),2,2,byrow=T))
# 4个选取ROI的方式,这里是先选出是face还是odor
for (val in unique(all_block$valance)) {
# 3个杏仁核的位置
for (pos in unique(all_block$position)) {
cat("\n作差并取绝对值之后\n")
# 进行方差分析
# 选择对应的数据
data_anova <- subset(all_block,position==pos & valance==val,select = c(1:14,16))
data_anova$condition <- paste0('condition_',data_anova$condition)
#更改变量名称
name <- paste(rep(c('Face_F','Face_H'),each=4),
rep(c('Odor_P','Odor_U'),each=2,times=2),
rep(c('Visi_I','Visi_V'),times=4),
sep = '_')
name2 <- paste(rep(c('Face_F','Face_H'),each=2),rep(c('Odor_P','Odor_U'),times=2),sep = '_')
#重命名
names(data_anova) <- c('Sub',name,'Count',name2,'condition')
# 处理没有做差的部分
data_anova4 <- data_anova[c(1,11:15)]
#更换数据格式
data_anova4 <- melt(data_anova4,c('Sub','condition'))
data_anova4 <- dcast(data_anova4,Sub~condition+variable)
#方差分析
MANOVA(data_anova4,dvs =names(data_anova4[-1]),
dvs.pattern ='condition_(..)_Face_(.)_Odor_(.)',
within=c('Condition','Face','Odor'))
# %>%
# EMMEANS("Odor", by="Face")
# 简单效应
# MANOVA(data_anova4[seq(2,9,2)],dvs =names(data_anova4[seq(2,9,2)]),
# dvs.pattern ='condition_(..)_Face_(.)',
# within=c('Condition','Face'))
#
cat("\n全部8个条件\n")
# 进行方差分析
# 选择对应的数据没有做差的部分
print(paste0(pos, val))
data_anova8 <- data_anova[c(1:9,15)]
#更换数据格式
data_anova8 <- melt(data_anova8,c('Sub','condition'))
data_anova8 <- dcast(data_anova8,Sub~condition+variable)
#方差分析
MANOVA(data_anova8,dvs =names(data_anova8[-1]),
dvs.pattern ='condition_(..)_Face_(.)_Odor_(.)_Visi_(.)',
within=c('Condition','Face','Odor','Visi'))
cat("\n绘图\n")
# face或者odor对应的条件
condition <- unique(subset(all_block,valance==val)$condition)
# 图的标题
title <- paste0(pos, val)
# 提取需要画图的部分
datachosen <- subset(analyze,group1==pos & group2%in%condition
& vars%in%c('FP','FU','HP','HU'),select = c(mean,se,vars,group2))
# Error bars represent standard error of the mean
figure_4 <- ggplot(datachosen, aes(x=vars, y=mean, fill=group2)) +
#coord_cartesian(ylim=c(0,0.4)) + # 设置y轴坐标范围
labs(title = title ,x='Condition',y='Mean β',fill='ROI')+#设置坐标轴
theme(axis.text.x = element_text(size=WORD_SIZE), # 设置x轴字体大小，以下同理
axis.text.y = element_text(size=WORD_SIZE),
axis.title.x = element_text(size=WORD_SIZE),
axis.title.y = element_text(size=WORD_SIZE),
legend.title = element_text(size=WORD_SIZE),
legend.text = element_text(size=WORD_SIZE),
plot.title = element_text(hjust = 0.5)) +
scale_y_continuous(expand = c(0,0))+
geom_bar(position="dodge", stat="identity") +
# scale_fill_manual(values = colors[1:2])+ #颜色
scale_fill_brewer(palette = "Set2",direction = -1)+ #颜色
geom_errorbar(aes(ymin=mean-se, ymax=mean+se),
width=.2,color='black', # Width of the error bars
position=position_dodge(.9))
# 8个条件没有作差的
datachosen <- subset(analyze,group1==pos & group2%in%condition
& vars%in%c('FPI','FPV','FUI','FUV','HPI','HPV','HUI','HUV'),
select = c(mean,se,vars,group2))
# Error bars represent standard error of the mean
figure_8 <- ggplot(datachosen, aes(x=vars, y=mean, fill=group2)) +
coord_cartesian(ylim=c(-0.5,0.5)) + # 设置y轴坐标范围
labs(title = title ,x='Condition',y='Mean β',fill='ROI')+#设置坐标轴
theme(axis.text.x = element_text(size=WORD_SIZE), # 设置x轴字体大小，以下同理
axis.text.y = element_text(size=WORD_SIZE),
axis.title.x = element_text(size=WORD_SIZE),
axis.title.y = element_text(size=WORD_SIZE),
legend.title = element_text(size=WORD_SIZE),
legend.text = element_text(size=WORD_SIZE),
plot.title = element_text(hjust = 0.5)) +
scale_y_continuous(expand = c(0,0))+
geom_bar(position="dodge", stat="identity") +
# scale_fill_manual(values = colors[1:2])+ #颜色
scale_fill_brewer(palette = "Set2",direction = -1)+ #颜色
geom_errorbar(aes(ymin=mean-se, ymax=mean+se),
width=.2,color='black', # Width of the error bars
position=position_dodge(.9))
# TENT
# 选择数据
# datachosen <- subset(all,position==pos & valance==val,select = c(1:12,14,16))
# 作差之后的结果
pd <- position_dodge(0.9)
datachosen <- subset(analyze_tent,group1==pos & group2%in%condition
& group3%in%c('FP','FU','HP','HU'),
select = c(mean,se,vars,group2,group3))
figtent_4 <- ggplot(datachosen, aes(x=vars, y=mean,
group=interaction(group2,group3),color=group3)) +
labs(x='TR',y='Mean β',color='Condition')+#设置坐标轴,linetype='ROI'
# scale_color_brewer(palette = "Set2",direction = -1)+ #颜色
scale_color_manual(values = gf_color[seq(2,8,2)])+ #自选颜色
# scale_linetype_manual(values=c("solid", "longdash"))+
facet_wrap(~group2,ncol = 1,scales="free")+#分面 +facet_wrap(~cyl,ncol-1,scales="free")
theme(axis.text.x = element_text(size=WORD_SIZE), # 设置x轴字体大小，以下同理
axis.text.y = element_text(size=WORD_SIZE),
axis.title.x = element_text(size=WORD_SIZE),
axis.title.y = element_text(size=WORD_SIZE),
legend.title = element_text(size=WORD_SIZE),
legend.text = element_text(size=WORD_SIZE),
text = element_text(size=WORD_SIZE),
plot.title = element_text(hjust = 0.5))+
scale_x_discrete(labels=as.character(0:10),expand = c(0,0))+
geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=.1,position = pd) +
geom_line(position = pd) +
geom_point(position = pd)
# 没有作差的结果
datachosen <- subset(analyze_tent,group1==pos & group2%in%condition
& group3%in%c('FPI','FPV','FUI','FUV','HPI','HPV','HUI','HUV'),
select = c(mean,se,vars,group2,group3))
figtent_8 <- ggplot(datachosen, aes(x=vars, y=mean,
group=interaction(group2,group3),color=group3)) +
labs(x='TR',y='Mean β',color='Condition')+#设置坐标轴,linetype='ROI'
# scale_color_brewer(palette = "Set2",direction = -1)+ #颜色
scale_color_manual(values = gf_color)+ #自选颜色
# scale_linetype_manual(values=c("solid", "longdash"))+
facet_wrap(~group2,ncol = 1,scales="free")+#分面 +facet_wrap(~cyl,ncol-1,scales="free")
theme(axis.text.x = element_text(size=WORD_SIZE), # 设置x轴字体大小，以下同理
axis.text.y = element_text(size=WORD_SIZE),
axis.title.x = element_text(size=WORD_SIZE),
axis.title.y = element_text(size=WORD_SIZE),
legend.title = element_text(size=WORD_SIZE),
legend.text = element_text(size=WORD_SIZE),
text = element_text(size=WORD_SIZE),
plot.title = element_text(hjust = 0.5))+
scale_x_discrete(labels=as.character(0:10),expand = c(0,0))+
geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=.1,position = pd) +
geom_line(position = pd) +
geom_point(position = pd)
# 合并几个图到一个里面
# ggarrange(p2,ggarrange(p1,p3,ncol=2,labels=c("B","C")),nrow=2,labels="A")
block <- ggarrange(figure_4,figure_8,ncol = 2,labels=c("A","B"),widths = c(3,4))
tent <- ggarrange(figtent_4,figtent_8,ncol = 2,labels=c("C","D"))
print(ggarrange(block,tent,nrow = 2,heights = c(1,3)))
# 测试保存图片
# jpeg(file="myplot.jpeg",width = 1920,height = 1080)
# ggarrange(figure_4,figure_8,ncol = 2,labels=c("A","B"),widths = c(1,2))
# dev.off()
}
}
##
## 作差并取绝对值之后
## ====== MANOVA Output (Within-Subjects Design) ======
##
## Descriptive Statistics:
## ─────────────────────────────────
## Condition Face Odor Mean S.D. N
## ─────────────────────────────────
## FH F P 0.25 0.13 20
## FH F U 0.30 0.16 20
## FH H P 0.22 0.12 20
## FH H U 0.15 0.09 20
## HF F P 0.15 0.08 20
## HF F U 0.30 0.23 20
## HF H P 0.33 0.20 20
## HF H U 0.31 0.27 20
## ─────────────────────────────────
## Total sample size: N = 20
##
## ANOVA Table:
## Dependent variable(s): condition_FH_Face_F_Odor_P, condition_FH_Face_F_Odor_U, condition_FH_Face_H_Odor_P, condition_FH_Face_H_Odor_U, condition_HF_Face_F_Odor_P, condition_HF_Face_F_Odor_U, condition_HF_Face_H_Odor_P, condition_HF_Face_H_Odor_U
## Between-subjects factor(s): -
## Within-subjects factor(s): Condition, Face, Odor
## Covariate(s): -
## ─────────────────────────────────────────────────────────────────────────────
## MS MSE df1 df2 F p η²p [90% CI]
## ─────────────────────────────────────────────────────────────────────────────
## Condition 0.088 0.032 1 19 2.79 .111 0.128 [0.000, 0.349]
## Face 0.001 0.030 1 19 0.02 .876 0.001 [0.000, 0.080]
## Odor 0.029 0.007 1 19 3.93 .062 . 0.171 [0.000, 0.393]
## Condition:Face 0.354 0.037 1 19 9.68 .006 ** 0.337 [0.068, 0.535]
## Condition:Odor 0.056 0.036 1 19 1.55 .229 0.075 [0.000, 0.287]
## Face:Odor 0.220 0.021 1 19 10.31 .005 ** 0.352 [0.077, 0.546]
## Condition:Face:Odor 0.004 0.033 1 19 0.13 .721 0.007 [0.000, 0.146]
## ─────────────────────────────────────────────────────────────────────────────
## MSE = Mean Square Error (an estimate of the population variance σ²)
##
## ANOVA Effect Size:
## ω² η² η²[G] η²[p] Cohen's f
## Condition 0.010 0.017 0.019 0.128 0.383
## Face -0.006 0.000 0.000 0.001 0.032
## Odor -0.001 0.005 0.006 0.171 0.454
## Condition:Face 0.060 0.066 0.072 0.337 0.713
## Condition:Odor 0.004 0.011 0.012 0.075 0.285
## Face:Odor 0.035 0.041 0.046 0.352 0.737
## Condition:Face:Odor -0.005 0.001 0.001 0.007 0.084
##
## ω² = omega-squared = (SS - df1 * MSE) / (SST + MSE)
## η² = eta-squared = SS / SST
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## Cohen’s f = sqrt( η²p / (1 - η²p) )
##
## Levene’s Test for Homogeneity of Variance:
##
## Mauchly’s Test of Sphericity:
##
##
## 全部8个条件
## [1] "AmyFace"
## ====== MANOVA Output (Within-Subjects Design) ======
##
## Descriptive Statistics:
## ───────────────────────────────────────
## Condition Face Odor Visi Mean S.D. N
## ───────────────────────────────────────
## FH F P I 0.02 0.18 20
## FH F P V 0.26 0.18 20
## FH F U I -0.13 0.15 20
## FH F U V 0.17 0.15 20
## FH H P I -0.07 0.13 20
## FH H P V -0.27 0.17 20
## FH H U I -0.09 0.13 20
## FH H U V -0.20 0.15 20
## HF F P I -0.11 0.20 20
## HF F P V -0.20 0.15 20
## HF F U I -0.08 0.19 20
## HF F U V -0.36 0.23 20
## HF H P I -0.06 0.16 20
## HF H P V 0.27 0.17 20
## HF H U I -0.11 0.20 20
## HF H U V 0.19 0.23 20
## ───────────────────────────────────────
## Total sample size: N = 20
##
## ANOVA Table:
## Dependent variable(s): condition_FH_Face_F_Odor_P_Visi_I, condition_FH_Face_F_Odor_P_Visi_V, condition_FH_Face_F_Odor_U_Visi_I, condition_FH_Face_F_Odor_U_Visi_V, condition_FH_Face_H_Odor_P_Visi_I, condition_FH_Face_H_Odor_P_Visi_V, condition_FH_Face_H_Odor_U_Visi_I, condition_FH_Face_H_Odor_U_Visi_V, condition_HF_Face_F_Odor_P_Visi_I, condition_HF_Face_F_Odor_P_Visi_V, condition_HF_Face_F_Odor_U_Visi_I, condition_HF_Face_F_Odor_U_Visi_V, condition_HF_Face_H_Odor_P_Visi_I, condition_HF_Face_H_Odor_P_Visi_V, condition_HF_Face_H_Odor_U_Visi_I, condition_HF_Face_H_Odor_U_Visi_V
## Between-subjects factor(s): -
## Within-subjects factor(s): Condition, Face, Odor, Visi
## Covariate(s): -
## ───────────────────────────────────────────────────────────────────────────────────
## MS MSE df1 df2 F p η²p [90% CI]
## ───────────────────────────────────────────────────────────────────────────────────
## Condition 0.029 0.072 1 19 0.39 .537 0.020 [0.000, 0.195]
## Face 0.014 0.020 1 19 0.67 .424 0.034 [0.000, 0.224]
## Odor 0.256 0.055 1 19 4.62 .045 * 0.196 [0.003, 0.416]
## Visi 0.300 0.030 1 19 10.05 .005 ** 0.346 [0.073, 0.542]
## Condition:Face 5.036 0.012 1 19 431.00 <.001 *** 0.958 [0.914, 0.971]
## Condition:Odor 0.003 0.027 1 19 0.13 .727 0.007 [0.000, 0.144]
## Face:Odor 0.106 0.017 1 19 6.26 .022 * 0.248 [0.022, 0.462]
## Condition:Visi 0.002 0.011 1 19 0.14 .708 0.008 [0.000, 0.150]
## Face:Visi 0.036 0.017 1 19 2.08 .166 0.099 [0.000, 0.316]
## Odor:Visi 0.005 0.027 1 19 0.20 .661 0.010 [0.000, 0.163]
## Condition:Face:Odor 0.097 0.010 1 19 9.97 .005 ** 0.344 [0.072, 0.540]
## Condition:Face:Visi 4.288 0.015 1 19 284.33 <.001 *** 0.937 [0.873, 0.957]
## Condition:Odor:Visi 0.181 0.017 1 19 10.42 .004 ** 0.354 [0.078, 0.548]
## Face:Odor:Visi 0.042 0.026 1 19 1.58 .224 0.077 [0.000, 0.289]
## Condition:Face:Odor:Visi 0.022 0.007 1 19 3.09 .095 . 0.140 [0.000, 0.361]
## ───────────────────────────────────────────────────────────────────────────────────
## MSE = Mean Square Error (an estimate of the population variance σ²)
##
## ANOVA Effect Size:
## ω² η² η²[G] η²[p] Cohen's f
## Condition 0.001 0.001 0.003 0.020 0.143
## Face 0.000 0.001 0.001 0.034 0.188
## Odor 0.013 0.013 0.026 0.196 0.494
## Visi 0.015 0.015 0.031 0.346 0.727
## Condition:Face 0.253 0.253 0.347 0.958 4.776
## Condition:Odor 0.000 0.000 0.000 0.007 0.084
## Face:Odor 0.005 0.005 0.011 0.248 0.574
## Condition:Visi 0.000 0.000 0.000 0.008 0.090
## Face:Visi 0.001 0.002 0.004 0.099 0.331
## Odor:Visi 0.000 0.000 0.001 0.010 0.101
## Condition:Face:Odor 0.005 0.005 0.010 0.344 0.724
## Condition:Face:Visi 0.215 0.216 0.312 0.937 3.857
## Condition:Odor:Visi 0.009 0.009 0.019 0.354 0.740
## Face:Odor:Visi 0.002 0.002 0.004 0.077 0.289
## Condition:Face:Odor:Visi 0.001 0.001 0.002 0.140 0.403
##
## ω² = omega-squared = (SS - df1 * MSE) / (SST + MSE)
## η² = eta-squared = SS / SST
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## Cohen’s f = sqrt( η²p / (1 - η²p) )
##
## Levene’s Test for Homogeneity of Variance:
##
## Mauchly’s Test of Sphericity:
##
##
## 绘图
##
## 作差并取绝对值之后
## ====== MANOVA Output (Within-Subjects Design) ======
##
## Descriptive Statistics:
## ─────────────────────────────────
## Condition Face Odor Mean S.D. N
## ─────────────────────────────────
## FH F P 0.18 0.11 20
## FH F U 0.25 0.13 20
## FH H P 0.20 0.13 20
## FH H U 0.14 0.09 20
## HF F P 0.13 0.09 20
## HF F U 0.18 0.13 20
## HF H P 0.25 0.19 20
## HF H U 0.24 0.15 20
## ─────────────────────────────────
## Total sample size: N = 20
##
## ANOVA Table:
## Dependent variable(s): condition_FH_Face_F_Odor_P, condition_FH_Face_F_Odor_U, condition_FH_Face_H_Odor_P, condition_FH_Face_H_Odor_U, condition_HF_Face_F_Odor_P, condition_HF_Face_F_Odor_U, condition_HF_Face_H_Odor_P, condition_HF_Face_H_Odor_U
## Between-subjects factor(s): -
## Within-subjects factor(s): Condition, Face, Odor
## Covariate(s): -
## ────────────────────────────────────────────────────────────────────────────
## MS MSE df1 df2 F p η²p [90% CI]
## ────────────────────────────────────────────────────────────────────────────
## Condition 0.001 0.008 1 19 0.17 .689 0.009 [0.000, 0.156]
## Face 0.024 0.018 1 19 1.32 .264 0.065 [0.000, 0.273]
## Odor 0.007 0.013 1 19 0.53 .475 0.027 [0.000, 0.210]
## Condition:Face 0.165 0.020 1 19 8.48 .009 ** 0.309 [0.051, 0.512]
## Condition:Odor 0.001 0.024 1 19 0.04 .847 0.002 [0.000, 0.097]
## Face:Odor 0.109 0.016 1 19 6.77 .018 * 0.263 [0.028, 0.475]
## Condition:Face:Odor 0.010 0.023 1 19 0.45 .510 0.023 [0.000, 0.201]
## ────────────────────────────────────────────────────────────────────────────
## MSE = Mean Square Error (an estimate of the population variance σ²)
##
## ANOVA Effect Size:
## ω² η² η²[G] η²[p] Cohen's f
## Condition -0.007 0.000 0.000 0.009 0.095
## Face 0.000 0.008 0.009 0.065 0.264
## Odor -0.006 0.002 0.003 0.027 0.167
## Condition:Face 0.049 0.057 0.060 0.309 0.669
## Condition:Odor -0.008 0.000 0.000 0.002 0.045
## Face:Odor 0.030 0.038 0.041 0.263 0.597
## Condition:Face:Odor -0.004 0.004 0.004 0.023 0.153
##
## ω² = omega-squared = (SS - df1 * MSE) / (SST + MSE)
## η² = eta-squared = SS / SST
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## Cohen’s f = sqrt( η²p / (1 - η²p) )
##
## Levene’s Test for Homogeneity of Variance:
##
## Mauchly’s Test of Sphericity:
##
##
## 全部8个条件
## [1] "lateralAmyFace"
## ====== MANOVA Output (Within-Subjects Design) ======
##
## Descriptive Statistics:
## ───────────────────────────────────────
## Condition Face Odor Visi Mean S.D. N
## ───────────────────────────────────────
## FH F P I 0.00 0.15 20
## FH F P V 0.17 0.10 20
## FH F U I -0.11 0.13 20
## FH F U V 0.14 0.12 20
## FH H P I -0.07 0.12 20
## FH H P V -0.25 0.16 20
## FH H U I -0.08 0.14 20
## FH H U V -0.17 0.11 20
## HF F P I -0.11 0.18 20
## HF F P V -0.18 0.16 20
## HF F U I -0.08 0.12 20
## HF F U V -0.24 0.14 20
## HF H P I -0.07 0.11 20
## HF H P V 0.18 0.17 20
## HF H U I -0.05 0.15 20
## HF H U V 0.18 0.14 20
## ───────────────────────────────────────
## Total sample size: N = 20
##
## ANOVA Table:
## Dependent variable(s): condition_FH_Face_F_Odor_P_Visi_I, condition_FH_Face_F_Odor_P_Visi_V, condition_FH_Face_F_Odor_U_Visi_I, condition_FH_Face_F_Odor_U_Visi_V, condition_FH_Face_H_Odor_P_Visi_I, condition_FH_Face_H_Odor_P_Visi_V, condition_FH_Face_H_Odor_U_Visi_I, condition_FH_Face_H_Odor_U_Visi_V, condition_HF_Face_F_Odor_P_Visi_I, condition_HF_Face_F_Odor_P_Visi_V, condition_HF_Face_F_Odor_U_Visi_I, condition_HF_Face_F_Odor_U_Visi_V, condition_HF_Face_H_Odor_P_Visi_I, condition_HF_Face_H_Odor_P_Visi_V, condition_HF_Face_H_Odor_U_Visi_I, condition_HF_Face_H_Odor_U_Visi_V
## Between-subjects factor(s): -
## Within-subjects factor(s): Condition, Face, Odor, Visi
## Covariate(s): -
## ───────────────────────────────────────────────────────────────────────────────────
## MS MSE df1 df2 F p η²p [90% CI]
## ───────────────────────────────────────────────────────────────────────────────────
## Condition 0.000 0.070 1 19 0.00 .979 0.000 [0.000, 1.000]
## Face 0.008 0.006 1 19 1.36 .258 0.067 [0.000, 0.275]
## Odor 0.010 0.014 1 19 0.71 .410 0.036 [0.000, 0.228]
## Visi 0.196 0.014 1 19 13.68 .002 ** 0.419 [0.126, 0.597]
## Condition:Face 3.205 0.010 1 19 325.74 <.001 *** 0.945 [0.888, 0.962]
## Condition:Odor 0.003 0.011 1 19 0.29 .598 0.015 [0.000, 0.180]
## Face:Odor 0.094 0.007 1 19 13.09 .002 ** 0.408 [0.118, 0.589]
## Condition:Visi 0.011 0.010 1 19 1.13 .301 0.056 [0.000, 0.260]
## Face:Visi 0.000 0.005 1 19 0.05 .829 0.003 [0.000, 0.107]
## Odor:Visi 0.006 0.018 1 19 0.32 .581 0.016 [0.000, 0.184]
## Condition:Face:Odor 0.039 0.008 1 19 4.76 .042 * 0.200 [0.004, 0.420]
## Condition:Face:Visi 2.499 0.010 1 19 242.28 <.001 *** 0.927 [0.853, 0.950]
## Condition:Odor:Visi 0.098 0.013 1 19 7.40 .014 * 0.280 [0.036, 0.489]
## Face:Odor:Visi 0.012 0.016 1 19 0.77 .392 0.039 [0.000, 0.232]
## Condition:Face:Odor:Visi 0.007 0.006 1 19 1.20 .287 0.059 [0.000, 0.265]
## ───────────────────────────────────────────────────────────────────────────────────
## MSE = Mean Square Error (an estimate of the population variance σ²)
##
## ANOVA Effect Size:
## ω² η² η²[G] η²[p] Cohen's f
## Condition 0.000 0.000 0.000 0.000 0.000
## Face 0.000 0.001 0.001 0.067 0.268
## Odor 0.000 0.001 0.002 0.036 0.193
## Visi 0.016 0.016 0.032 0.419 0.849
## Condition:Face 0.266 0.266 0.354 0.945 4.145
## Condition:Odor 0.000 0.000 0.001 0.015 0.123
## Face:Odor 0.007 0.008 0.016 0.408 0.830
## Condition:Visi 0.000 0.001 0.002 0.056 0.244
## Face:Visi 0.000 0.000 0.000 0.003 0.055
## Odor:Visi 0.000 0.000 0.001 0.016 0.128
## Condition:Face:Odor 0.003 0.003 0.007 0.200 0.500
## Condition:Face:Visi 0.207 0.208 0.299 0.927 3.564
## Condition:Odor:Visi 0.008 0.008 0.016 0.280 0.624
## Face:Odor:Visi 0.001 0.001 0.002 0.039 0.201
## Condition:Face:Odor:Visi 0.000 0.001 0.001 0.059 0.250
##
## ω² = omega-squared = (SS - df1 * MSE) / (SST + MSE)
## η² = eta-squared = SS / SST
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## Cohen’s f = sqrt( η²p / (1 - η²p) )
##
## Levene’s Test for Homogeneity of Variance:
##
## Mauchly’s Test of Sphericity:
##
##
## 绘图
##
## 作差并取绝对值之后
## ====== MANOVA Output (Within-Subjects Design) ======
##
## Descriptive Statistics:
## ─────────────────────────────────
## Condition Face Odor Mean S.D. N
## ─────────────────────────────────
## FH F P 0.29 0.16 20
## FH F U 0.31 0.19 20
## FH H P 0.23 0.14 20
## FH H U 0.17 0.11 20
## HF F P 0.21 0.10 20
## HF F U 0.37 0.30 20
## HF H P 0.40 0.25 20
## HF H U 0.37 0.41 20
## ─────────────────────────────────
## Total sample size: N = 20
##
## ANOVA Table:
## Dependent variable(s): condition_FH_Face_F_Odor_P, condition_FH_Face_F_Odor_U, condition_FH_Face_H_Odor_P, condition_FH_Face_H_Odor_U, condition_HF_Face_F_Odor_P, condition_HF_Face_F_Odor_U, condition_HF_Face_H_Odor_P, condition_HF_Face_H_Odor_U
## Between-subjects factor(s): -
## Within-subjects factor(s): Condition, Face, Odor
## Covariate(s): -
## ────────────────────────────────────────────────────────────────────────────
## MS MSE df1 df2 F p η²p [90% CI]
## ────────────────────────────────────────────────────────────────────────────
## Condition 0.282 0.079 1 19 3.59 .073 . 0.159 [0.000, 0.381]
## Face 0.000 0.052 1 19 0.00 .982 0.000 [0.000, 1.000]
## Odor 0.022 0.015 1 19 1.47 .240 0.072 [0.000, 0.282]
## Condition:Face 0.396 0.049 1 19 8.04 .011 * 0.297 [0.045, 0.503]
## Condition:Odor 0.070 0.051 1 19 1.38 .255 0.068 [0.000, 0.276]
## Face:Odor 0.185 0.028 1 19 6.56 .019 * 0.257 [0.025, 0.469]
## Condition:Face:Odor 0.034 0.040 1 19 0.85 .369 0.043 [0.000, 0.239]
## ────────────────────────────────────────────────────────────────────────────
## MSE = Mean Square Error (an estimate of the population variance σ²)
##
## ANOVA Effect Size:
## ω² η² η²[G] η²[p] Cohen's f
## Condition 0.027 0.031 0.034 0.159 0.435
## Face -0.004 0.000 0.000 0.000 0.000
## Odor -0.002 0.002 0.003 0.072 0.279
## Condition:Face 0.039 0.044 0.047 0.297 0.650
## Condition:Odor 0.003 0.008 0.009 0.068 0.270
## Face:Odor 0.016 0.021 0.023 0.257 0.588
## Condition:Face:Odor -0.001 0.004 0.004 0.043 0.212
##
## ω² = omega-squared = (SS - df1 * MSE) / (SST + MSE)
## η² = eta-squared = SS / SST
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## Cohen’s f = sqrt( η²p / (1 - η²p) )
##
## Levene’s Test for Homogeneity of Variance:
##
## Mauchly’s Test of Sphericity:
##
##
## 全部8个条件
## [1] "medialAmyFace"
## ====== MANOVA Output (Within-Subjects Design) ======
##
## Descriptive Statistics:
## ───────────────────────────────────────
## Condition Face Odor Visi Mean S.D. N
## ───────────────────────────────────────
## FH F P I 0.02 0.20 20
## FH F P V 0.30 0.21 20
## FH F U I -0.13 0.18 20
## FH F U V 0.18 0.18 20
## FH H P I -0.07 0.14 20
## FH H P V -0.28 0.19 20
## FH H U I -0.09 0.15 20
## FH H U V -0.20 0.19 20
## HF F P I -0.07 0.30 20
## HF F P V -0.19 0.20 20
## HF F U I -0.06 0.30 20
## HF F U V -0.40 0.27 20
## HF H P I -0.05 0.20 20
## HF H P V 0.34 0.23 20
## HF H U I -0.11 0.31 20
## HF H U V 0.22 0.30 20
## ───────────────────────────────────────
## Total sample size: N = 20
##
## ANOVA Table:
## Dependent variable(s): condition_FH_Face_F_Odor_P_Visi_I, condition_FH_Face_F_Odor_P_Visi_V, condition_FH_Face_F_Odor_U_Visi_I, condition_FH_Face_F_Odor_U_Visi_V, condition_FH_Face_H_Odor_P_Visi_I, condition_FH_Face_H_Odor_P_Visi_V, condition_FH_Face_H_Odor_U_Visi_I, condition_FH_Face_H_Odor_U_Visi_V, condition_HF_Face_F_Odor_P_Visi_I, condition_HF_Face_F_Odor_P_Visi_V, condition_HF_Face_F_Odor_U_Visi_I, condition_HF_Face_F_Odor_U_Visi_V, condition_HF_Face_H_Odor_P_Visi_I, condition_HF_Face_H_Odor_P_Visi_V, condition_HF_Face_H_Odor_U_Visi_I, condition_HF_Face_H_Odor_U_Visi_V
## Between-subjects factor(s): -
## Within-subjects factor(s): Condition, Face, Odor, Visi
## Covariate(s): -
## ───────────────────────────────────────────────────────────────────────────────────
## MS MSE df1 df2 F p η²p [90% CI]
## ───────────────────────────────────────────────────────────────────────────────────
## Condition 0.004 0.130 1 19 0.03 .863 0.002 [0.000, 0.089]
## Face 0.011 0.049 1 19 0.23 .640 0.012 [0.000, 0.169]
## Odor 0.446 0.071 1 19 6.23 .022 * 0.247 [0.021, 0.461]
## Visi 0.356 0.046 1 19 7.66 .012 * 0.287 [0.040, 0.495]
## Condition:Face 5.661 0.026 1 19 214.78 <.001 *** 0.919 [0.836, 0.944]
## Condition:Odor 0.027 0.037 1 19 0.75 .398 0.038 [0.000, 0.231]
## Face:Odor 0.134 0.028 1 19 4.75 .042 * 0.200 [0.004, 0.420]
## Condition:Visi 0.000 0.019 1 19 0.00 .980 0.000 [0.000, 1.000]
## Face:Visi 0.105 0.050 1 19 2.10 .164 0.099 [0.000, 0.316]
## Odor:Visi 0.022 0.035 1 19 0.64 .434 0.032 [0.000, 0.221]
## Condition:Face:Odor 0.106 0.014 1 19 7.34 .014 * 0.279 [0.035, 0.488]
## Condition:Face:Visi 5.550 0.042 1 19 133.36 <.001 *** 0.875 [0.753, 0.914]
## Condition:Odor:Visi 0.211 0.020 1 19 10.38 .004 ** 0.353 [0.078, 0.547]
## Face:Odor:Visi 0.056 0.047 1 19 1.21 .285 0.060 [0.000, 0.266]
## Condition:Face:Odor:Visi 0.011 0.014 1 19 0.78 .389 0.039 [0.000, 0.233]
## ───────────────────────────────────────────────────────────────────────────────────
## MSE = Mean Square Error (an estimate of the population variance σ²)
##
## ANOVA Effect Size:
## ω² η² η²[G] η²[p] Cohen's f
## Condition 0.000 0.000 0.000 0.002 0.045
## Face 0.000 0.000 0.001 0.012 0.110
## Odor 0.015 0.016 0.028 0.247 0.573
## Visi 0.012 0.013 0.022 0.287 0.634
## Condition:Face 0.198 0.199 0.265 0.919 3.368
## Condition:Odor 0.000 0.001 0.002 0.038 0.199
## Face:Odor 0.004 0.005 0.008 0.200 0.500
## Condition:Visi 0.000 0.000 0.000 0.000 0.000
## Face:Visi 0.003 0.004 0.007 0.099 0.331
## Odor:Visi 0.000 0.001 0.001 0.032 0.182
## Condition:Face:Odor 0.003 0.004 0.007 0.279 0.622
## Condition:Face:Visi 0.195 0.195 0.261 0.875 2.646
## Condition:Odor:Visi 0.007 0.007 0.013 0.353 0.739
## Face:Odor:Visi 0.001 0.002 0.004 0.060 0.253
## Condition:Face:Odor:Visi 0.000 0.000 0.001 0.039 0.201
##
## ω² = omega-squared = (SS - df1 * MSE) / (SST + MSE)
## η² = eta-squared = SS / SST
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## Cohen’s f = sqrt( η²p / (1 - η²p) )
##
## Levene’s Test for Homogeneity of Variance:
##
## Mauchly’s Test of Sphericity:
##
##
## 绘图
##
## 作差并取绝对值之后
## ====== MANOVA Output (Within-Subjects Design) ======
##
## Descriptive Statistics:
## ─────────────────────────────────
## Condition Face Odor Mean S.D. N
## ─────────────────────────────────
## PU F P 0.32 0.17 20
## PU F U 0.22 0.17 20
## PU H P 0.34 0.19 20
## PU H U 0.22 0.17 20
## UP F P 0.26 0.28 20
## UP F U 0.32 0.17 20
## UP H P 0.36 0.34 20
## UP H U 0.40 0.44 20
## ─────────────────────────────────
## Total sample size: N = 20
##
## ANOVA Table:
## Dependent variable(s): condition_PU_Face_F_Odor_P, condition_PU_Face_F_Odor_U, condition_PU_Face_H_Odor_P, condition_PU_Face_H_Odor_U, condition_UP_Face_F_Odor_P, condition_UP_Face_F_Odor_U, condition_UP_Face_H_Odor_P, condition_UP_Face_H_Odor_U
## Between-subjects factor(s): -
## Within-subjects factor(s): Condition, Face, Odor
## Covariate(s): -
## ─────────────────────────────────────────────────────────────────────────────
## MS MSE df1 df2 F p η²p [90% CI]
## ─────────────────────────────────────────────────────────────────────────────
## Condition 0.144 0.119 1 19 1.21 .285 0.060 [0.000, 0.266]
## Face 0.095 0.061 1 19 1.57 .226 0.076 [0.000, 0.288]
## Odor 0.036 0.018 1 19 2.06 .167 0.098 [0.000, 0.315]
## Condition:Face 0.071 0.053 1 19 1.34 .262 0.066 [0.000, 0.274]
## Condition:Odor 0.253 0.024 1 19 10.69 .004 ** 0.360 [0.082, 0.552]
## Face:Odor 0.004 0.021 1 19 0.21 .649 0.011 [0.000, 0.166]
## Condition:Face:Odor 0.000 0.033 1 19 0.00 .964 0.000 [0.000, 1.000]
## ─────────────────────────────────────────────────────────────────────────────
## MSE = Mean Square Error (an estimate of the population variance σ²)
##
## ANOVA Effect Size:
## ω² η² η²[G] η²[p] Cohen's f
## Condition 0.010 0.013 0.014 0.060 0.253
## Face 0.006 0.009 0.009 0.076 0.287
## Odor 0.000 0.003 0.004 0.098 0.330
## Condition:Face 0.004 0.007 0.007 0.066 0.266
## Condition:Odor 0.020 0.023 0.024 0.360 0.750
## Face:Odor -0.003 0.000 0.000 0.011 0.105
## Condition:Face:Odor -0.003 0.000 0.000 0.000 0.000
##
## ω² = omega-squared = (SS - df1 * MSE) / (SST + MSE)
## η² = eta-squared = SS / SST
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## Cohen’s f = sqrt( η²p / (1 - η²p) )
##
## Levene’s Test for Homogeneity of Variance:
##
## Mauchly’s Test of Sphericity:
##
##
## 全部8个条件
## [1] "AmyOdor"
## ====== MANOVA Output (Within-Subjects Design) ======
##
## Descriptive Statistics:
## ───────────────────────────────────────
## Condition Face Odor Visi Mean S.D. N
## ───────────────────────────────────────
## PU F P I -0.04 0.21 20
## PU F P V 0.28 0.17 20
## PU F U I -0.09 0.13 20
## PU F U V -0.28 0.15 20
## PU H P I -0.08 0.15 20
## PU H P V 0.26 0.19 20
## PU H U I -0.05 0.18 20
## PU H U V -0.23 0.18 20
## UP F P I 0.06 0.27 20
## UP F P V -0.18 0.16 20
## UP F U I -0.11 0.16 20
## UP F U V 0.20 0.12 20
## UP H P I 0.02 0.29 20
## UP H P V -0.33 0.15 20
## UP H U I -0.10 0.28 20
## UP H U V 0.29 0.23 20
## ───────────────────────────────────────
## Total sample size: N = 20
##
## ANOVA Table:
## Dependent variable(s): condition_PU_Face_F_Odor_P_Visi_I, condition_PU_Face_F_Odor_P_Visi_V, condition_PU_Face_F_Odor_U_Visi_I, condition_PU_Face_F_Odor_U_Visi_V, condition_PU_Face_H_Odor_P_Visi_I, condition_PU_Face_H_Odor_P_Visi_V, condition_PU_Face_H_Odor_U_Visi_I, condition_PU_Face_H_Odor_U_Visi_V, condition_UP_Face_F_Odor_P_Visi_I, condition_UP_Face_F_Odor_P_Visi_V, condition_UP_Face_F_Odor_U_Visi_I, condition_UP_Face_F_Odor_U_Visi_V, condition_UP_Face_H_Odor_P_Visi_I, condition_UP_Face_H_Odor_P_Visi_V, condition_UP_Face_H_Odor_U_Visi_I, condition_UP_Face_H_Odor_U_Visi_V
## Between-subjects factor(s): -
## Within-subjects factor(s): Condition, Face, Odor, Visi
## Covariate(s): -
## ───────────────────────────────────────────────────────────────────────────────────
## MS MSE df1 df2 F p η²p [90% CI]
## ───────────────────────────────────────────────────────────────────────────────────
## Condition 0.008 0.034 1 19 0.22 .641 0.012 [0.000, 0.168]
## Face 0.004 0.032 1 19 0.14 .712 0.007 [0.000, 0.149]
## Odor 0.178 0.036 1 19 4.89 .039 * 0.205 [0.006, 0.424]
## Visi 0.210 0.017 1 19 12.52 .002 ** 0.397 [0.109, 0.581]
## Condition:Face 0.015 0.009 1 19 1.66 .213 0.081 [0.000, 0.293]
## Condition:Odor 4.012 0.031 1 19 130.51 <.001 *** 0.873 [0.748, 0.912]
## Face:Odor 0.226 0.024 1 19 9.51 .006 ** 0.334 [0.065, 0.532]
## Condition:Visi 0.045 0.008 1 19 5.35 .032 * 0.220 [0.011, 0.437]
## Face:Visi 0.000 0.021 1 19 0.01 .936 0.000 [0.000, 0.024]
## Odor:Visi 0.101 0.062 1 19 1.63 .217 0.079 [0.000, 0.291]
## Condition:Face:Odor 0.028 0.010 1 19 2.83 .109 0.130 [0.000, 0.350]
## Condition:Face:Visi 0.005 0.012 1 19 0.37 .552 0.019 [0.000, 0.191]
## Condition:Odor:Visi 6.838 0.110 1 19 62.25 <.001 *** 0.766 [0.559, 0.840]
## Face:Odor:Visi 0.042 0.033 1 19 1.26 .275 0.062 [0.000, 0.269]
## Condition:Face:Odor:Visi 0.053 0.032 1 19 1.67 .212 0.081 [0.000, 0.294]
## ───────────────────────────────────────────────────────────────────────────────────
## MSE = Mean Square Error (an estimate of the population variance σ²)
##
## ANOVA Effect Size:
## ω² η² η²[G] η²[p] Cohen's f
## Condition -0.001 0.000 0.001 0.012 0.110
## Face -0.001 0.000 0.000 0.007 0.084
## Odor 0.006 0.008 0.015 0.205 0.508
## Visi 0.008 0.009 0.018 0.397 0.811
## Condition:Face -0.001 0.001 0.001 0.081 0.297
## Condition:Odor 0.170 0.172 0.258 0.873 2.622
## Face:Odor 0.008 0.010 0.019 0.334 0.708
## Condition:Visi 0.001 0.002 0.004 0.220 0.531
## Face:Visi -0.001 0.000 0.000 0.000 0.000
## Odor:Visi 0.003 0.004 0.009 0.079 0.293
## Condition:Face:Odor 0.000 0.001 0.002 0.130 0.387
## Condition:Face:Visi -0.001 0.000 0.000 0.019 0.139
## Condition:Odor:Visi 0.291 0.293 0.372 0.766 1.809
## Face:Odor:Visi 0.000 0.002 0.004 0.062 0.257
## Condition:Face:Odor:Visi 0.001 0.002 0.005 0.081 0.297
##
## ω² = omega-squared = (SS - df1 * MSE) / (SST + MSE)
## η² = eta-squared = SS / SST
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## Cohen’s f = sqrt( η²p / (1 - η²p) )
##
## Levene’s Test for Homogeneity of Variance:
##
## Mauchly’s Test of Sphericity:
##
##
## 绘图
##
## 作差并取绝对值之后
## ====== MANOVA Output (Within-Subjects Design) ======
##
## Descriptive Statistics:
## ─────────────────────────────────
## Condition Face Odor Mean S.D. N
## ─────────────────────────────────
## PU F P 0.31 0.17 20
## PU F U 0.19 0.13 20
## PU H P 0.27 0.14 20
## PU H U 0.16 0.13 20
## UP F P 0.21 0.15 20
## UP F U 0.26 0.17 20
## UP H P 0.29 0.14 20
## UP H U 0.23 0.11 20
## ─────────────────────────────────
## Total sample size: N = 20
##
## ANOVA Table:
## Dependent variable(s): condition_PU_Face_F_Odor_P, condition_PU_Face_F_Odor_U, condition_PU_Face_H_Odor_P, condition_PU_Face_H_Odor_U, condition_UP_Face_F_Odor_P, condition_UP_Face_F_Odor_U, condition_UP_Face_H_Odor_P, condition_UP_Face_H_Odor_U
## Between-subjects factor(s): -
## Within-subjects factor(s): Condition, Face, Odor
## Covariate(s): -
## ────────────────────────────────────────────────────────────────────────────
## MS MSE df1 df2 F p η²p [90% CI]
## ────────────────────────────────────────────────────────────────────────────
## Condition 0.011 0.037 1 19 0.29 .595 0.015 [0.000, 0.180]
## Face 0.001 0.022 1 19 0.04 .835 0.002 [0.000, 0.104]
## Odor 0.140 0.015 1 19 9.47 .006 ** 0.333 [0.065, 0.531]
## Condition:Face 0.036 0.026 1 19 1.41 .250 0.069 [0.000, 0.278]
## Condition:Odor 0.117 0.015 1 19 7.93 .011 * 0.294 [0.043, 0.501]
## Face:Odor 0.019 0.007 1 19 2.65 .120 0.122 [0.000, 0.342]
## Condition:Face:Odor 0.034 0.018 1 19 1.86 .188 0.089 [0.000, 0.304]
## ────────────────────────────────────────────────────────────────────────────
## MSE = Mean Square Error (an estimate of the population variance σ²)
##
## ANOVA Effect Size:
## ω² η² η²[G] η²[p] Cohen's f
## Condition -0.002 0.003 0.003 0.015 0.123
## Face -0.005 0.000 0.000 0.002 0.045
## Odor 0.034 0.039 0.042 0.333 0.707
## Condition:Face 0.005 0.010 0.011 0.069 0.272
## Condition:Odor 0.028 0.033 0.035 0.294 0.645
## Face:Odor 0.000 0.005 0.006 0.122 0.373
## Condition:Face:Odor 0.004 0.009 0.010 0.089 0.313
##
## ω² = omega-squared = (SS - df1 * MSE) / (SST + MSE)
## η² = eta-squared = SS / SST
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## Cohen’s f = sqrt( η²p / (1 - η²p) )
##
## Levene’s Test for Homogeneity of Variance:
##
## Mauchly’s Test of Sphericity:
##
##
## 全部8个条件
## [1] "lateralAmyOdor"
## ====== MANOVA Output (Within-Subjects Design) ======
##
## Descriptive Statistics:
## ───────────────────────────────────────
## Condition Face Odor Visi Mean S.D. N
## ───────────────────────────────────────
## PU F P I -0.09 0.13 20
## PU F P V 0.22 0.12 20
## PU F U I -0.07 0.10 20
## PU F U V -0.22 0.13 20
## PU H P I -0.06 0.12 20
## PU H P V 0.19 0.16 20
## PU H U I -0.06 0.15 20
## PU H U V -0.17 0.14 20
## UP F P I 0.00 0.24 20
## UP F P V -0.15 0.16 20
## UP F U I -0.11 0.17 20
## UP F U V 0.14 0.13 20
## UP H P I -0.07 0.22 20
## UP H P V -0.30 0.13 20
## UP H U I -0.04 0.17 20
## UP H U V 0.18 0.12 20
## ───────────────────────────────────────
## Total sample size: N = 20
##
## ANOVA Table:
## Dependent variable(s): condition_PU_Face_F_Odor_P_Visi_I, condition_PU_Face_F_Odor_P_Visi_V, condition_PU_Face_F_Odor_U_Visi_I, condition_PU_Face_F_Odor_U_Visi_V, condition_PU_Face_H_Odor_P_Visi_I, condition_PU_Face_H_Odor_P_Visi_V, condition_PU_Face_H_Odor_U_Visi_I, condition_PU_Face_H_Odor_U_Visi_V, condition_UP_Face_F_Odor_P_Visi_I, condition_UP_Face_F_Odor_P_Visi_V, condition_UP_Face_F_Odor_U_Visi_I, condition_UP_Face_F_Odor_U_Visi_V, condition_UP_Face_H_Odor_P_Visi_I, condition_UP_Face_H_Odor_P_Visi_V, condition_UP_Face_H_Odor_U_Visi_I, condition_UP_Face_H_Odor_U_Visi_V
## Between-subjects factor(s): -
## Within-subjects factor(s): Condition, Face, Odor, Visi
## Covariate(s): -
## ───────────────────────────────────────────────────────────────────────────────────
## MS MSE df1 df2 F p η²p [90% CI]
## ───────────────────────────────────────────────────────────────────────────────────
## Condition 0.012 0.044 1 19 0.27 .608 0.014 [0.000, 0.177]
## Face 0.005 0.012 1 19 0.41 .532 0.021 [0.000, 0.196]
## Odor 0.009 0.018 1 19 0.49 .494 0.025 [0.000, 0.205]
## Visi 0.181 0.018 1 19 10.16 .005 ** 0.348 [0.075, 0.543]
## Condition:Face 0.040 0.006 1 19 6.41 .020 * 0.252 [0.023, 0.466]
## Condition:Odor 2.705 0.013 1 19 207.62 <.001 *** 0.916 [0.831, 0.942]
## Face:Odor 0.217 0.020 1 19 11.03 .004 ** 0.367 [0.087, 0.558]
## Condition:Visi 0.057 0.015 1 19 3.74 .068 . 0.165 [0.000, 0.386]
## Face:Visi 0.017 0.017 1 19 1.00 .331 0.050 [0.000, 0.251]
## Odor:Visi 0.001 0.023 1 19 0.06 .810 0.003 [0.000, 0.115]
## Condition:Face:Odor 0.084 0.013 1 19 6.54 .019 * 0.256 [0.025, 0.469]
## Condition:Face:Visi 0.008 0.005 1 19 1.66 .213 0.080 [0.000, 0.293]
## Condition:Odor:Visi 3.491 0.018 1 19 191.67 <.001 *** 0.910 [0.819, 0.938]
## Face:Odor:Visi 0.027 0.020 1 19 1.37 .256 0.067 [0.000, 0.276]
## Condition:Face:Odor:Visi 0.002 0.017 1 19 0.13 .722 0.007 [0.000, 0.146]
## ───────────────────────────────────────────────────────────────────────────────────
## MSE = Mean Square Error (an estimate of the population variance σ²)
##
## ANOVA Effect Size:
## ω² η² η²[G] η²[p] Cohen's f
## Condition 0.000 0.001 0.002 0.014 0.119
## Face -0.001 0.000 0.001 0.021 0.146
## Odor -0.001 0.001 0.001 0.025 0.160
## Visi 0.012 0.013 0.025 0.348 0.731
## Condition:Face 0.002 0.003 0.006 0.252 0.580
## Condition:Odor 0.191 0.193 0.274 0.916 3.302
## Face:Odor 0.014 0.015 0.029 0.367 0.761
## Condition:Visi 0.003 0.004 0.008 0.165 0.445
## Face:Visi 0.000 0.001 0.002 0.050 0.229
## Odor:Visi -0.001 0.000 0.000 0.003 0.055
## Condition:Face:Odor 0.005 0.006 0.012 0.256 0.587
## Condition:Face:Visi -0.001 0.001 0.001 0.080 0.295
## Condition:Odor:Visi 0.247 0.249 0.327 0.910 3.180
## Face:Odor:Visi 0.001 0.002 0.004 0.067 0.268
## Condition:Face:Odor:Visi -0.001 0.000 0.000 0.007 0.084
##
## ω² = omega-squared = (SS - df1 * MSE) / (SST + MSE)
## η² = eta-squared = SS / SST
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## Cohen’s f = sqrt( η²p / (1 - η²p) )
##
## Levene’s Test for Homogeneity of Variance:
##
## Mauchly’s Test of Sphericity:
##
##
## 绘图
##
## 作差并取绝对值之后
## ====== MANOVA Output (Within-Subjects Design) ======
##
## Descriptive Statistics:
## ─────────────────────────────────
## Condition Face Odor Mean S.D. N
## ─────────────────────────────────
## PU F P 0.32 0.19 20
## PU F U 0.26 0.19 20
## PU H P 0.38 0.22 20
## PU H U 0.25 0.21 20
## UP F P 0.30 0.39 20
## UP F U 0.38 0.23 20
## UP H P 0.44 0.45 20
## UP H U 0.49 0.72 20
## ─────────────────────────────────
## Total sample size: N = 20
##
## ANOVA Table:
## Dependent variable(s): condition_PU_Face_F_Odor_P, condition_PU_Face_F_Odor_U, condition_PU_Face_H_Odor_P, condition_PU_Face_H_Odor_U, condition_UP_Face_F_Odor_P, condition_UP_Face_F_Odor_U, condition_UP_Face_H_Odor_P, condition_UP_Face_H_Odor_U
## Between-subjects factor(s): -
## Within-subjects factor(s): Condition, Face, Odor
## Covariate(s): -
## ────────────────────────────────────────────────────────────────────────────
## MS MSE df1 df2 F p η²p [90% CI]
## ────────────────────────────────────────────────────────────────────────────
## Condition 0.406 0.261 1 19 1.55 .228 0.076 [0.000, 0.287]
## Face 0.221 0.094 1 19 2.34 .142 0.110 [0.000, 0.329]
## Odor 0.009 0.028 1 19 0.33 .575 0.017 [0.000, 0.185]
## Condition:Face 0.090 0.112 1 19 0.80 .381 0.041 [0.000, 0.236]
## Condition:Odor 0.252 0.044 1 19 5.74 .027 * 0.232 [0.015, 0.448]
## Face:Odor 0.028 0.061 1 19 0.46 .504 0.024 [0.000, 0.203]
## Condition:Face:Odor 0.003 0.065 1 19 0.04 .842 0.002 [0.000, 0.100]
## ────────────────────────────────────────────────────────────────────────────
## MSE = Mean Square Error (an estimate of the population variance σ²)
##
## ANOVA Effect Size:
## ω² η² η²[G] η²[p] Cohen's f
## Condition 0.016 0.019 0.019 0.076 0.287
## Face 0.007 0.010 0.011 0.110 0.352
## Odor -0.003 0.000 0.000 0.017 0.132
## Condition:Face 0.001 0.004 0.004 0.041 0.207
## Condition:Odor 0.009 0.012 0.012 0.232 0.550
## Face:Odor -0.002 0.001 0.001 0.024 0.157
## Condition:Face:Odor -0.003 0.000 0.000 0.002 0.045
##
## ω² = omega-squared = (SS - df1 * MSE) / (SST + MSE)
## η² = eta-squared = SS / SST
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## Cohen’s f = sqrt( η²p / (1 - η²p) )
##
## Levene’s Test for Homogeneity of Variance:
##
## Mauchly’s Test of Sphericity:
##
##
## 全部8个条件
## [1] "medialAmyOdor"
## ====== MANOVA Output (Within-Subjects Design) ======
##
## Descriptive Statistics:
## ───────────────────────────────────────
## Condition Face Odor Visi Mean S.D. N
## ───────────────────────────────────────
## PU F P I -0.01 0.27 20
## PU F P V 0.31 0.20 20
## PU F U I -0.09 0.16 20
## PU F U V -0.30 0.18 20
## PU H P I -0.08 0.17 20
## PU H P V 0.30 0.20 20
## PU H U I -0.05 0.22 20
## PU H U V -0.26 0.20 20
## UP F P I 0.10 0.37 20
## UP F P V -0.18 0.19 20
## UP F U I -0.12 0.20 20
## UP F U V 0.26 0.14 20
## UP H P I 0.07 0.38 20
## UP H P V -0.36 0.18 20
## UP H U I -0.14 0.43 20
## UP H U V 0.34 0.35 20
## ───────────────────────────────────────
## Total sample size: N = 20
##
## ANOVA Table:
## Dependent variable(s): condition_PU_Face_F_Odor_P_Visi_I, condition_PU_Face_F_Odor_P_Visi_V, condition_PU_Face_F_Odor_U_Visi_I, condition_PU_Face_F_Odor_U_Visi_V, condition_PU_Face_H_Odor_P_Visi_I, condition_PU_Face_H_Odor_P_Visi_V, condition_PU_Face_H_Odor_U_Visi_I, condition_PU_Face_H_Odor_U_Visi_V, condition_UP_Face_F_Odor_P_Visi_I, condition_UP_Face_F_Odor_P_Visi_V, condition_UP_Face_F_Odor_U_Visi_I, condition_UP_Face_F_Odor_U_Visi_V, condition_UP_Face_H_Odor_P_Visi_I, condition_UP_Face_H_Odor_P_Visi_V, condition_UP_Face_H_Odor_U_Visi_I, condition_UP_Face_H_Odor_U_Visi_V
## Between-subjects factor(s): -
## Within-subjects factor(s): Condition, Face, Odor, Visi
## Covariate(s): -
## ──────────────────────────────────────────────────────────────────────────────────
## MS MSE df1 df2 F p η²p [90% CI]
## ──────────────────────────────────────────────────────────────────────────────────
## Condition 0.029 0.051 1 19 0.57 .460 0.029 [0.000, 0.214]
## Face 0.031 0.040 1 19 0.77 .390 0.039 [0.000, 0.233]
## Odor 0.319 0.064 1 19 5.01 .037 * 0.209 [0.007, 0.427]
## Visi 0.238 0.025 1 19 9.56 .006 ** 0.335 [0.066, 0.533]
## Condition:Face 0.033 0.013 1 19 2.46 .133 0.115 [0.000, 0.334]
## Condition:Odor 4.572 0.057 1 19 80.85 <.001 *** 0.810 [0.634, 0.869]
## Face:Odor 0.250 0.042 1 19 5.95 .025 * 0.239 [0.018, 0.454]
## Condition:Visi 0.025 0.012 1 19 2.17 .157 0.102 [0.000, 0.320]
## Face:Visi 0.000 0.037 1 19 0.00 .958 0.000 [0.000, 1.000]
## Odor:Visi 0.277 0.131 1 19 2.11 .162 0.100 [0.000, 0.317]
## Condition:Face:Odor 0.011 0.017 1 19 0.65 .430 0.033 [0.000, 0.222]
## Condition:Face:Visi 0.017 0.037 1 19 0.45 .509 0.023 [0.000, 0.202]
## Condition:Odor:Visi 9.068 0.216 1 19 41.98 <.001 *** 0.688 [0.438, 0.787]
## Face:Odor:Visi 0.043 0.069 1 19 0.62 .439 0.032 [0.000, 0.220]
## Condition:Face:Odor:Visi 0.132 0.053 1 19 2.48 .132 0.116 [0.000, 0.335]
## ──────────────────────────────────────────────────────────────────────────────────
## MSE = Mean Square Error (an estimate of the population variance σ²)
##
## ANOVA Effect Size:
## ω² η² η²[G] η²[p] Cohen's f
## Condition -0.001 0.001 0.001 0.029 0.173
## Face -0.001 0.001 0.002 0.039 0.201
## Odor 0.008 0.009 0.016 0.209 0.514
## Visi 0.005 0.007 0.012 0.335 0.710
## Condition:Face -0.001 0.001 0.002 0.115 0.360
## Condition:Odor 0.130 0.132 0.188 0.810 2.065
## Face:Odor 0.006 0.007 0.013 0.239 0.560
## Condition:Visi -0.001 0.001 0.001 0.102 0.337
## Face:Visi -0.002 0.000 0.000 0.000 0.000
## Odor:Visi 0.006 0.008 0.014 0.100 0.333
## Condition:Face:Odor -0.001 0.000 0.001 0.033 0.185
## Condition:Face:Visi -0.001 0.000 0.001 0.023 0.153
## Condition:Odor:Visi 0.259 0.261 0.315 0.688 1.485
## Face:Odor:Visi 0.000 0.001 0.002 0.032 0.182
## Condition:Face:Odor:Visi 0.002 0.004 0.007 0.116 0.362
##
## ω² = omega-squared = (SS - df1 * MSE) / (SST + MSE)
## η² = eta-squared = SS / SST
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## Cohen’s f = sqrt( η²p / (1 - η²p) )
##
## Levene’s Test for Homogeneity of Variance:
##
## Mauchly’s Test of Sphericity:
##
##
## 绘图
# # 可以查看对应主题的颜色
# colour_plot(swatch())
# colour_plot(ggthemr("pale"))
# colour_plot(ggthemr("greyscale"))
# colour_plot(ggthemr("solarized"))
# # colorbrewer选择颜色，生成颜色
# display.brewer.all_block()
# colors <- brewer.pal(8,"Set2")
# colour_plot(colors)
# # colors是自带颜色函数
# colors()
